scholarly journals EXPLORING SPATIO-TEMPORAL CLUSTER FOR IDENTIFY DENGUE CLUSTER IN URBAN AREA OF INDONESIA

2019 ◽  
Vol 6 (1) ◽  
Keyword(s):  
Urban Area ◽  
Temporal Cluster ◽  
Spatio Temporal

scholarly journals A spatio-temporal cluster analysis of structurally deficient bridges in the contiguous USA

2020 ◽  
Vol 4 ◽  
pp. 100034
Author(s):  
R.W. Amin ◽  
S. Kocak ◽  
H.E. Sevil ◽  
G.P. Peterson ◽  
J.T. Hamilton ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Temporal Cluster ◽  
Spatio Temporal

Risk analysis of veterinary drug residues in aquatic products in the Yangtze River Delta of China

2021 ◽  
Author(s):  
Sijing Xia ◽  
Bing Niu ◽  
Jiahui Chen ◽  
Xiaojun Deng ◽  
Qin Chen
Keyword(s):  
Yangtze River ◽  
Yangtze River Delta ◽  
River Delta ◽  
Veterinary Drug ◽  
Veterinary Drug Residues ◽  
Drug Residues ◽  
Aquatic Products ◽  
Temporal Cluster ◽  
Spatio Temporal ◽  
The Yangtze River Delta

Aquatic products are favored by people all over the world, but the potential quality and safety issues cannot be ignored. In order to determine the risk of veterinary drug residues in aquatic products in the Yangtze River Delta, this paper used the Geographic Information System (GIS) method to analyze Chinese veterinary drugs in aquatic products in Shanghai, Jiangsu, Zhejiang, and Anhui (Yangtze River Delta Urban Agglomerations) from 2017 to 2019. The spatial distribution pattern, hotspot detection and analysis and spatio-temporal cluster analysis of the residual excess rate and detection rate were studied. The results showed that the overall excess rate and detection rate of veterinary drug residues in aquatic products from 2017 to 2019 showed a spatial random distribution. The result of hotspot analysis and spatio-temporal cluster analysis showed that the rate of detection of veterinary drug residues and the rate of detection of residues in excess of regulatory standards were clustered. This study can provide a scientific basis for food safety evaluation and risk management suggestions.

Spatio-temporal disparity between demand and supply of park green space service in urban area of Wuhan from 2000 to 2014

2018 ◽  
Vol 71 ◽  
pp. 49-59 ◽  
Author(s):  
Lijun Xing ◽  
Yanfang Liu ◽  
Xingjian Liu ◽  
Xiaojian Wei ◽  
Yan Mao
Keyword(s):  
Urban Area ◽  
Green Space ◽  
Demand And Supply ◽  
Spatio Temporal ◽  
Temporal Disparity

Spatio-temporal cluster analyses of landslides in Italy at national and regional scale

2020 ◽  
Author(s):  
Marj Tonini ◽  
Kim Romailler ◽  
Gaetano Pecoraro ◽  
Michele Calvello
Keyword(s):  
Cluster Analysis ◽  
Point Processes ◽  
Statistical Significance ◽  
Regional Scale ◽  
Regional Analysis ◽  
Scan Statistics ◽  
Economic Losses ◽  
Campania Region ◽  
Temporal Cluster ◽  
Spatio Temporal

<p><strong>Keywords:</strong> Landslides, FraneItalia, cluster analysis, spatio-temporal point process</p><p>In Italy landslides pose a significant and widespread risk, resulting in a large number of casualties and huge economic losses. Landslide inventories are critical to support investigations of where and when landslides have happened and may occur in the future, i.e. to establish reliable correlations between triggering factors and landslide occurrences. To deal with this issue, statistical methods originally developed for spatio-temporal stochastic point processes can be useful for identifying correlations between events in space and time and detecting a significant excess of cases within large landslide datasets.</p><p>In the present study, the authors propose an approach to analyze and visualize spatio-temporal clusters of landslides occurred in Italy in the period 2010-2017, considering the weather warning zones as territorial units. Besides, a regional analysis was conducted in Campania region considering the municipalities as territorial units. Data on landslide occurrences derived from the FraneItalia catalog, an inventory retrieved from online Italian news. The database contains 8931 landslides, grouped in 4231 single events and 938 areal events (records referring to multiple landslides triggered by the same cause in the same geographic area). Analyses were performed both annually, considering each year individually, and globally, considering the entire frame period. We applied the spatio-temporal scan statistics permutation model (STPSS, integrated in SaTScan<sup>TM</sup> software), which allowed detecting clusters’ location and estimating their statistical significance. STPSS is based on cylindrical moving windows which scan the area across the space and in time counting the number of observed and expected occurrences and computing the likelihood ratio. The statistical inference (p-value) is evaluated by Monte Carlo sampling and finally the most likely clusters in the real and randomly generated datasets are compared.</p><p>Although more detailed analyses are required for the determination of cause-effect relationships among landslides and other variables, some relations with the local topographic and meteorological conditions can already be argued. At national scale, spatio-temporal clusters of landslides are mainly recurrent in two zones: the area enclosing Liguria Region – Northern Tuscany at north-west and the area between Abruzzo and Molise regions at centre-east. During the year, landslide clusters are particularly abundant between October and March. as most of the events in the FraneItalia catalog are rainfall-induced, strongly influenced by seasonal rainfall patterns. Concerning the regional analysis, most of the clusters are located in the Lattari mountains, the Pizzo d’Alvano massif and the Picentini mountains, areas highly susceptible to landslide occurrence due to geomorphological factors.</p><p>In conclusion, the application of spatio-temporal cluster analysis at various scale allowed the identification of frame periods with greater landslide activity. The question of whether this increase in activity depends climate conditions or topographic factors is still open and request further investigations.</p><p>REFERENCES</p><p>Calvello, M., Pecoraro, G. FraneItalia: a catalog of recent Italian landslides. <em>Geoenvironmental Disasters</em>. 5: 13 (2018)</p><p>Tonini, M. & Cama, M. Spatio-temporal pattern distribution of landslides causing damage in Switzerland. <em>Landslides</em> 16 (2019)</p>

Spatio-temporal variability of methane (CH4 ) concentrations and diffusive fluxes from a tropical coastal embayment surrounded by a large urban area (Guanabara Bay, Rio de Janeiro, Brazil)

2016 ◽  
Vol 61 (S1) ◽  
pp. S238-S252 ◽  
Author(s):  
Luiz C. Cotovicz ◽  
Bastiaan A. Knoppers ◽  
Nilva Brandini ◽  
Dominique Poirier ◽  
Suzan J. Costa Santos ◽  
...  
Keyword(s):  
Urban Area ◽  
Temporal Variability ◽  
Rio De Janeiro ◽  
Guanabara Bay ◽  
Large Urban Area ◽  
Coastal Embayment ◽  
Spatio Temporal ◽  
Diffusive Fluxes

scholarly journals Conglomeration of General Linear Model for Epilepsy Clinical Neuroimaging

2020 ◽  
pp. 1-12
Author(s):  
Ibrahim Abubakar Sadiq ◽  
Jyoti S. Raghav ◽  
Sanjeev Kumar Sharma
Keyword(s):  
Expectation Maximization ◽  
Variance Component ◽  
Statistical Parametric Mapping ◽  
General Linear Model ◽  
Pool Data ◽  
Group Level ◽  
Temporal Cluster ◽  
Neuroimaging Data ◽  
Component Estimation ◽  
Spatio Temporal

An innovative standard scheme was established aimed at developing inferences and interpretations statistically relative to clinical neuroimaging facts and figures. It involves as particular instances, SPMs, a standard methodology to clinical neuroimaging anatomy. Our developed model contributes and provides various educational and statistical benefits which begin from the anatomy of facts at group level before the level of the voxel, commencing by direct modelling of the location and shape of the modules. We set out a new general framework for making inferences from neuroimaging data, which includes a standard approach to neuroimaging analysis, statistical parametric mapping (SPM), as a particular case. The model offers numerous conceptual and statistical advantages that begin from analysis of the collected data at the group level somewhat than the voxel level, from explicit modelling of the shape and position of clusters of activation. It provides a natural and moral way to pool data from nearby voxels for parameter and variance-component estimation. The model can also be viewed as performing Spatio-temporal cluster analysis. The parameters of the model are estimated using an expectation-maximization (EM) algorithm.

scholarly journals Covid-19 Space-time Cluster Detection Using Retrospective Analysis

2022 ◽  
Author(s):  
KALEAB TESFAYE TEGEGNE ◽  
ELENI TESFAYE TEGEGNE ◽  
MEKIBIB KASSA TESSEMA ◽  
GELETA ABERA ◽  
BERHANU BIFATO ◽  
...  
Keyword(s):  
Addis Ababa ◽  
Probability Model ◽  
Health Data ◽  
Space Time ◽  
Scan Statistics ◽  
County Level ◽  
Temporal Cluster ◽  
Preventative Measures ◽  
Spatio Temporal ◽  
Time Scan

Abstract Background: As of the 31st of January 2021, there had been 102,399,513 confirmed cases of COVID-19 worldwide, with 2,217,005 deaths reported to WHOThe goal of this study is to uncover the spatiotemporal patterns of COVID 19 in Ethiopia, which will aid in the planning and implementation of essential preventative measures. Methods We obtained data on COVID 19 cases reported in Ethiopia from November 23 to December 29, 2021, from an Ethiopian health data website that is open to the public.Kulldorff's retrospective space-time scan statistics were utilized to detect the temporal, geographical, and spatiotemporal clusters of COVID 19 at the county level in Ethiopia, using the discrete Poisson probability model. Results: In Ethiopia, between November 23 and December 29, 2021, a total of 22,199 COVID 19 cases were reported.The COVID 19 cases in Ethiopia were strongly clustered in spatial, temporal, and spatiotemporal distribution, according to the results of Kulldorff's scan. statisticsThe most likely Spatio-temporal cluster (LLR = 70369.783209, RR = 412.48, P 0.001) was mostly concentrated in Addis Ababa and clustered between 2021/11/1 and 2021/11/30.Conclusion: From November 23 to December 29, 2021, this study found three large COVID 19 space-time clusters in Ethiopia, which could aid in future resource allocation in high-risk locations for COVID 19 management and prevention.

scholarly journals Prospective Spatio-Temporal and Temporal Cluster Detection by Salmonella Serotype

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Eric R. Peterson ◽  
Vasudha Reddy ◽  
HaeNa Waechter ◽  
Lan Li ◽  
Kristen Forney ◽  
...  
Keyword(s):  
Cluster Detection ◽  
Temporal Cluster ◽  
Spatio Temporal ◽  
Salmonella Serotype
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